{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/367608"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/367608","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Data-driven linear interatomic potentials","abstract":"Modelling alloy phase transitions at the atomic scale with first-principles accuracy has been a long-standing research goal. This is becoming increasingly accessible due to the rapid development of data-driven interatomic potential frameworks over the last decade. This thesis presents two novel frameworks, namely 'atomic permutation invariant polynomials' and 'atomic cluster expansion'. Both frameworks are linear models that can be used to create computationally efficient potentials that approximate the potential energy surface for materials and molecules with first-principles accuracy while preserving its underlying symmetries. In addition, this thesis introduces an automated method for building training databases for data-driven interatomic potentials, called 'hyper-active learning'. This method is a pioneering approach that accelerates traditional active learning techniques by biasing (molecular dynamics) simulations towards uncertainty. This novel method is shown to reduce the number of exploratory timesteps required to build a suitable training database by up to an order of magnitude. Finally, a workflow for modelling alloy phase transitions with first-principles accuracy is presented. This workflow involves driving nested sampling simulations using previously mentioned data-driven interatomic potentials to approximate the partition function with first-principles accuracy. From the partition function, free energies and specific heat capacities are then computed, and used to locate first-order phase transitions. These results show good agreement with experimental observations, and are consistent with physically observed phenomena such as the presence of dual-phase micro-structures and precipitate formation in multi-component alloys.","abstract_html":"Modelling alloy phase transitions at the atomic scale with first-principles accuracy has been a long-standing research goal. This is becoming increasingly accessible due to the rapid development of data-driven interatomic potential frameworks over the last decade. This thesis presents two novel frameworks, namely 'atomic permutation invariant polynomials' and 'atomic cluster expansion'. Both frameworks are linear models that can be used to create computationally efficient potentials that approximate the potential energy surface for materials and molecules with first-principles accuracy while preserving its underlying symmetries. In addition, this thesis introduces an automated method for building training databases for data-driven interatomic potentials, called 'hyper-active learning'. This method is a pioneering approach that accelerates traditional active learning techniques by biasing (molecular dynamics) simulations towards uncertainty. This novel method is shown to reduce the number of exploratory timesteps required to build a suitable training database by up to an order of magnitude. Finally, a workflow for modelling alloy phase transitions with first-principles accuracy is presented. This workflow involves driving nested sampling simulations using previously mentioned data-driven interatomic potentials to approximate the partition function with first-principles accuracy. From the partition function, free energies and specific heat capacities are then computed, and used to locate first-order phase transitions. These results show good agreement with experimental observations, and are consistent with physically observed phenomena such as the presence of dual-phase micro-structures and precipitate formation in multi-component alloys.","abstract_has_math":false,"creators":["van der Oord, Cas"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Csanyi, Gabor"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-03","date_published":"2023-01-03","updated_at":"2026-07-22T22:24:06Z","subjects":["alloys","interatomic potentials"],"languages":["eng"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c3ddd717-2709-48ae-b2e7-a741589684a1/download","https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.108147","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Csanyi, Gabor"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Dassault Systemes UK EPSRC"]},{"key":"dc:creator","label":"Author","values":["van der Oord, Cas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023-01-03"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/367608"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["alloys","interatomic potentials"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c3ddd717-2709-48ae-b2e7-a741589684a1/download","https://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.108147"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/d9c87b17-3ae9-432c-bfdc-d584f85c7b32/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Modelling alloy phase transitions at the atomic scale with first-principles accuracy has been a long-standing research goal. 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